Bidirectional synaptic plasticity rapidly modifies hippocampal representations.
Bidirectional synaptic plasticity rapidly modifies hippocampal representations.
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DOI：
10.7554/elife.73046
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发表时间：
2021-12-09
期刊：
影响因子：
7.7
通讯作者：
Romani S
中科院分区：
文献类型：
--
作者：
Milstein AD;Li Y;Bittner KC;Grienberger C;Soltesz I;Magee JC;Romani S
学习需要神经适应，这被认为是由活动依赖性突触可塑性介导的。一个相对非标准形式的突触可塑性驱动的树突状钙尖峰，或高原电位，已被报道的啮齿动物海马CA1区神经元的位置场形成的基础。在这里，我们发现，这种行为时标突触可塑性（BTSP）也可以重塑现有的位置字段通过双向突触权重的变化，取决于时间接近的平台电位预先存在的位置字段。当诱发附近的一个现有的位置场，高原电位诱导较少的突触增强和更多的抑郁症，这表明BTSP可能是负相关的突触后激活。然而，操纵的位置细胞膜电位和计算建模表明，这种反相关实际上是从依赖于当前的突触权重，使弱输入增强和强输入抑制。实现这种双向突触学习规则的网络模型表明，BTSP使群体活动而不是成对的神经元相关性能够驱动神经适应体验。在熟悉的社区开发新的住宅区，一个错误的转弯，最终延长了周日的散步：我们对世界的内部表征需要不断更新，我们需要能够将相隔很长时间的事件联系起来，以微调未来的结果。这通常需要改变神经连接。一个被称为海马体的大脑区域参与构建和维护我们的环境地图。然而，当身体处于特定位置时，来自其他大脑区域的信号可以通过触发称为树突状钙峰的细胞事件来激活海马体中沉默的神经元。Milstein等人探索了海马体中的树突状钙峰是否也可以帮助大脑更新其世界地图，使神经元在一个位置停止活跃，并开始在新的位置做出反应。小鼠实验表明，钙峰可以通过加强或削弱特定细胞之间的连接来改变单个神经元对环境的反应特征。至关重要的是，这种机制允许神经元将在较长时间尺度上展开的事件序列与日常生活中遇到的事件序列联系起来。然后将一个计算模型放在一起，它表明海马体中的树突状钙峰可以使大脑在未来做出更好的空间决策。事实上，这些尖峰信号是由参与复杂认知过程的大脑区域的输入驱动的，这可能使导航选择的延迟结果能够指导神经元活动和布线的变化。总体而言，Milstein等人的工作推进了对大脑学习和记忆的理解，并可能为设计更好的人工学习系统提供信息。
Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here, we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience. A new housing development in a familiar neighborhood, a wrong turn that ends up lengthening a Sunday stroll: our internal representation of the world requires constant updating, and we need to be able to associate events separated by long intervals of time to finetune future outcome. This often requires neural connections to be altered. A brain region known as the hippocampus is involved in building and maintaining a map of our environment. However, signals from other brain areas can activate silent neurons in the hippocampus when the body is in a specific location by triggering cellular events called dendritic calcium spikes. Milstein et al. explored whether dendritic calcium spikes in the hippocampus could also help the brain to update its map of the world by enabling neurons to stop being active at one location and to start responding at a new position. Experiments in mice showed that calcium spikes could change which features of the environment individual neurons respond to by strengthening or weaking connections between specific cells. Crucially, this mechanism allowed neurons to associate event sequences that unfold over a longer timescale that was more relevant to the ones encountered in day-to-day life. A computational model was then put together, and it demonstrated that dendritic calcium spikes in the hippocampus could enable the brain to make better spatial decisions in future. Indeed, these spikes are driven by inputs from brain regions involved in complex cognitive processes, potentially enabling the delayed outcomes of navigational choices to guide changes in the activity and wiring of neurons. Overall, the work by Milstein et al. advances the understanding of learning and memory in the brain and may inform the design of better systems for artificial learning.